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An Investigation of Strategies in Baseball

Author

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  • George R. Lindsey

    (SACLANT ASW Research Center, La Spezia, Italy)

Abstract

The advisability of a particular strategy must be judged not only in terms of the situation on the bases and the number of men out, but also with regard to the inning and the score. Two sets of data taken from a large number of major league games are used to give (1) the dependence of the probability of winning the game on the score and inning, and (2) the distribution of runs scored between the arrival of a new batter at the plate in each of twenty-four situations and the end of the half-inning. The first shows that the runs adding most to the probability of winning are the one tying the score and the one putting the batting team one ahead. All other runs are slightly less valuable early in the game, and become increasingly less valuable as the game proceeds. By combining the two sets of data, the situations are determined in which an intentional base on balls, a double play allowing a run to score, a sacrifice, and an attempted steal are advisable strategies, if average players are concerned. An index of batting effectiveness based on the contribution to run production in average situations is developed.

Suggested Citation

  • George R. Lindsey, 1963. "An Investigation of Strategies in Baseball," Operations Research, INFORMS, vol. 11(4), pages 477-501, August.
    • Handle: RePEc:inm:oropre:v:11:y:1963:i:4:p:477-501
    DOI: 10.1287/opre.11.4.477
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    Cited by:

    1. Hirotsu Nobuyoshi & Bickel J. Eric, 2019. "Using a Markov decision process to model the value of the sacrifice bunt," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 15(4), pages 327-344, December.
    2. Young Lee, 2011. "Is the small-ball strategy effective in winning games? A stochastic frontier production approach," Journal of Productivity Analysis, Springer, vol. 35(1), pages 51-59, February.
    3. David J. Berri & John Charles Bradbury, 2010. "Working in the Land of the Metricians," Journal of Sports Economics, , vol. 11(1), pages 29-47, February.
    4. J. Eric Bickel, 2009. "On the Decision to Take a Pitch," Decision Analysis, INFORMS, vol. 6(3), pages 186-193, September.
    5. Sherer, Peter D., 2017. "When is it time to stop doing the same old thing? How institutional and organizational entrepreneurs changed Major League Baseball," Journal of Business Venturing, Elsevier, vol. 32(4), pages 355-370.
    6. Michal Friesl & Jan Libich & Petr Stehlík, 2020. "Fixing ice hockey’s low scoring flip side? Just flip the sides," Annals of Operations Research, Springer, vol. 292(1), pages 27-45, September.
    7. Wittkowski Knut M. & Song Tingting & Anderson Kent & Daniels John E., 2008. "U-Scores for Multivariate Data in Sports," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 4(3), pages 1-24, July.
    8. Hamrick Jeff & Rasp John, 2011. "Using Local Correlation to Explain Success in Baseball," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 7(4), pages 1-29, October.
    9. M B Wright, 2009. "50 years of OR in sport," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(1), pages 161-168, May.
    10. Michael J. Fry & Jeffrey W. Ohlmann, 2012. "Introduction to the Special Issue on Analytics in Sports, Part I: General Sports Applications," Interfaces, INFORMS, vol. 42(2), pages 105-108, April.
    11. Michal Friesl & Liam J. A. Lenten & Jan Libich & Petr Stehlík, 2017. "In search of goals: increasing ice hockey’s attractiveness by a sides swap," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(9), pages 1006-1018, September.

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